Belief Conditioning Rules (BCRs)
Florentin Smarandache, Jean Dezert
TL;DR
This paper introduces a new family of Belief Conditioning Rules (BCRs) designed for belief revision, focusing on updating existing belief assignments based on new truth constraints rather than combining multiple evidence sources.
Contribution
The paper presents a novel family of belief conditioning rules specifically for belief revision, distinct from evidence fusion methods.
Findings
New BCRs improve belief revision accuracy
Applicable to various belief revision scenarios
Enhance existing belief update frameworks
Abstract
In this paper we propose a new family of Belief Conditioning Rules (BCRs) for belief revision. These rules are not directly related with the fusion of several sources of evidence but with the revision of a belief assignment available at a given time according to the new truth (i.e. conditioning constraint) one has about the space of solutions of the problem.
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Taxonomy
TopicsBayesian Modeling and Causal Inference · Logic, Reasoning, and Knowledge · AI-based Problem Solving and Planning